This is the very first exercise of this course. It’s not too hard, its purpose is rather to get used to our exercise format - this HTML file, for instance - and more importantly, to get used to the tidyverse world.

Consequently, this exercise is really short and just let you play around with some pipes and tibbles. It’s a mini-exercise!

First things first: To work with the ‘tidyverse’ we have to have access to its packages.

1

Load the tidyverse library.

If the tidyverse library has not been installed yet, you can install it with the command install.packages("tidyverse").

solution 

if (!require(tidyverse)) install.packages("tidyverse")
library(tidyverse)

After successfully loading the tidyverse library we turn to the magic world of pipes. Remember, pipes are a convenient way to disentangle nested R functions and to write cleaner R code. First, have a look at the code in the following block:

mean(sqrt(as.numeric(read.csv2("../data/titanic/titanic.csv", sep = ",")$Fare)))

2

What do you think is the command doing?

It’s always a good approach to start from the inner command to the outer ones.

solution

  1. The titanic data are imported with read.csv2()
  2. Only the Fare variable is extracted using the $-sign
  3. The variable is converted to the numeric format with as.numeric()
  4. A square root transformation is applied with sqrt()
  5. The mean is calculated with mean()

Using the commands in such a way is not very clear, isn’t it? You have already learned that pipes provide an easy approach to navigate this issue.

3

Create a nice pipe from this nested command.

You can call individual columns of a piped object with .$col_name.

solution 

read.csv2("../data/titanic/titanic.csv", sep = ",") %>% 
  .$Fare %>% 
  as.numeric() %>% 
  sqrt() %>% 
  mean()
## [1] 10.46045

As we’ve learned, tidyverse is not all about pipes, it’s about tidy data. The default data format to have access to tidy data in tidyverse is the tibble format. In the previous task, you have already imported the titanic data, but it’s in the standard data.frame format.

4

Load the titanic dataset and convert it immediatly to a tibble.

base-R’s read.csv2() is your friend. Also, you may want to do it all at once in one pipe.

solution 

titanic_tibble <-
  read.csv2("../data/titanic/titanic.csv", sep = ",") %>% 
  as_tibble()

Now look at the following data.frame. It’s been created with the standard base-R tools. tidyverse also provides a feature, the tribble() command, to create small data tables as tibbles from scratch.

##   day amount_coffee words_written
## 1   1             2           245
## 2   2             5           691
## 3   3             1            10
## 4   4             8          2100
## 5   5             4           490

5

Use the tribble()-function to directly rebuild the data frame as a tibble.

Remember defining new columns with a preceding ~.

solution 

tribble(
  ~day,  ~amount_coffee,   ~words_written, 
  1,                 2,              245,
  2,                 5,              691,
  3,                 1,               10,
  4,                 8,             2100,
  5,                 4,              490
)
## # A tibble: 5 x 3
##     day amount_coffee words_written
##   <dbl>         <dbl>         <dbl>
## 1     1             2           245
## 2     2             5           691
## 3     3             1            10
## 4     4             8          2100
## 5     5             4           490